Preliminary Conference Agenda

Overview and details of the sessions of this conference. Please select a date or room to show only sessions at that day or location. Please select a single session for detailed view (with abstracts and downloads if available).

This agenda is preliminary and subject to change.

Session Overview
Papers 1: Scientific Work and Data Practices
Monday, 01/Apr/2019:
10:30am - 12:00pm

Session Chair: Michael Lesk, Rutgers University
Location: 2110/2111/2112

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Surfacing Data Change in Scientific Work

D. Paine, L. Ramakrishnan

Lawrence Berkeley National Laboratory, United States of America

Data are essential products of scientific work that move among and through research infrastructures over time. Data constantly changes due to evolving practices and knowledge, requiring improvisational work by scientists to determine the effects on analyses. Today for end users of datasets much of the information about changes, and the processes leading to them, is invisible — embedded elsewhere in the work of a collaboration. Simultaneously scientists use increasing quantities of data, making ad hoc approaches to identifying change difficult to scale effectively. Our research investigates data change by examining how scientists make sense of change in datasets being created and sustained by the collaborative infrastructures they engage with. We examine two forms of change, before examining how trust and project rhythms influence a scientist's notion that the newest available data are the best. We explore the opportunity to design tools and practices to support user examinations of data change and surface key provenance information embedded in research infrastructures.

Understanding Hackathons for Science: Collaboration, Affordances, and Outcomes

E. P. P. Pe-Than, J. D. Herbsleb

Carnegie Mellon University, United States of America

Nowadays, hackathons have become a popular way of bringing people together to engage in brief, intensive collaborative work. Despite being a brief activity, being collocated with team members and focused on a task—radical collocation—could improve collaboration of scientific software teams. Using a mixed-methods study of participants who attended two hackathons at Space Telescope Science Institute, we examined how hackathons can facilitate collaboration in scientific software teams which typically involve members from two different disciplines: science and software engineering. We found that hackathons created a focused interruption-free working environment in which team members were able to assess each other’s skills, focus together on a single project and leverage opportunities to exchange knowledge with other collocated participants, thereby allowing technical work to advance more efficiently. This study suggests “hacking” as a new and productive form of collaborative work in scientific software production.

A comparative study of biological scientists’ data sharing between genome sequence data and lab experiment data

Y. Kim

University of Kentucky, United States of America

This research aims to explore how the institutional pressure, resource, and individual motivation factors all affect biological scientists’ data sharing behaviors in different data types. This research utilized a combined theoretical framework including institutional theory and theory of planned behavior to examine institutional pressure, resource, and individual motivation factors influencing biological scientists’ data sharing intentions between different data types including genome sequence data and lab experiment data. A total of 342 survey responses from biological sciences were employed for a series of statistical analyses including Cronbach’s alpha, factor analysis, hierarchical regression, and t-test. This research shows that biological scientists’ data sharing intentions are led by institutional pressure, resource, and individual motivation factors, and the levels of those factors are significantly different between genome sequence data and lab experiment data. This research shows that biological scientists’ data sharing differs depending on the data they share, and different policies and support needs to be applied to encourage biological scientists’ data sharing of different data types.